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共 2194 条记录，以下是461-470 订阅

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BLS: A Learning Search Algorithm with Bayesian Learning 3

BLS: A Learning Search Algorithm with Bayesian Learning

引用

3rd IEEE international conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Zhang, Wei Northeastern Univ Coll Comp Sci & Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538643013

In learning search, it is assumed that a search algorithm has adequate opportunities to traverse the problem state space, and to sense and learn the topological (or probability) knowledge about the state space. In this paper, a new search algorithm based on Bayesian learning (algorithm BLS) is proposed, which finds the solution path for any given state pair (s, t) in the state space efficiently. The algorithm depicts the search as a process of pattern recognition for the current path. During its depth-first search, the BLS classifies the current path, depending on the probability distribution functions acquired in previous Bayesian learning, and prunes it if necessary. It is proved that the mean complexity of BLS grows linearly with the length of the solution path after sufficient training.

关键词： machine learning learning search big graph search and mining heuristic search Bayesian learning pattern recognition

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A Mobile Application for Early Detection of Melanoma by Image Processing Algorithms 25

A Mobile Application for Early Detection of Melanoma by Imag...

引用

25th Iranian conference on Biomedical Engineering / 3rd international Iranian conference on Biomedical Engineering (ICBME)

作者： Alizadeh, Seyed Mohammad Mahloojifar, Ali Tarbiat Modares Univ Dept Biomed Engn Tehran Iran

ISBN: (纸本)9781538679524

Melanoma is the most dangerous skin cancer which causes many deaths annually. However, early detection can help treat it. For accurate detection of melanoma, dermatologists use biopsy which is usually associated with pain, time and cost. With the advancement of technology and the development of smartphones, many mobile applications have been designed for early detection of melanoma. Although they are fast in the detection of melanoma and save time and money, they are not as accurate as the biopsy. In this paper, the authors proposed an application for early detection of melanoma using image processing methods and pattern recognition algorithms by Android Studio software, Java programming language, and the OpenCV library. All detection steps were carried out using the Android smartphone. For better performance in the classification step, in addition to the smartphone, a computer was also used. This application is user-friendly and the calculated Accuracy, Sensitivity, and Specificity are 95%, 98%, and 92.19% on average, respectively. It should be noted that these results are more reliable when the lesions are geometrically distinct.

关键词： melanoma image processing smartphone android pattern recognition algorithm OpenCV library

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Detection of 3D Mask in 2D Face recognition System Using DWT and LBP 3

Detection of 3D Mask in 2D Face Recognition System Using DWT...

引用

3rd IEEE international conference on Communication and Information Systems (ICCIS)

作者： Mahore, Arti Tripathi, Meenakshi Malaviya Natl Inst Technol Dept Comp Sci & Engn Jaipur Rajasthan India

ISBN: (纸本)9781538692738

Biometrics systems have replaced traditional ways of authenticating systems. The concept of face recognition based on the fact that every person has different biological attributes. Yet, these systems have a possibility to suffer from the various types of spoofing attacks. Spoofing is the process of fooling the system by impersonating a legitimate user to gain illegal access. Face verification systems are the easiest ones to suffer from spoof attack because facial pictures are easy to access. Spoofing attack can be classified into two types: 2D and 3D. 2D attacks faked by using the photos or video of the legitimate user and 3D attacks faked by using 3D masks. Here, We have introduced a new approach to detect presence of 3D mask based face anti-spoofing using frequency and texture based feature descriptors. The proposed approach extracts Local Binary pattern based texture features from discrete wavelet transformed images. We have evaluated the proposed approach on the 3D Mask Attack dataset that is available publicly. Our approach is performed better than state of art.

关键词： biometric mask DWT LBP

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Heptagonal Photonic Crystal Fiber Based Chemical Sensor in THz Regime

Heptagonal Photonic Crystal Fiber Based Chemical Sensor in T...

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international conference on Informatics, Electronics & Vision (ICIEV)

作者： Md. Mahamudul Hasan Shuvo Sen Md. Juwel Rana Bikash Kumar Paul Md. Ahsan Habib Golam Moktader Daiyan Kawsar Ahmed Dept. of Physics Mawlana Bhashani Science and Technology University Tangail Bangladesh Dept. of ICT Mawlana Bhashani Science and Technology University Tangail Bangladesh Dept. of Software Engineering (SWE) Daffodil International University Dhaka Bangladesh School of Computer Science and Technology University of Electronic Science and Technology Sichuan Province China Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Tangail Bangladesh

In this article, here is developed a novel TOPAS based heptagonal photonic crystal fiber (H-PCF) for the identification of various chemical substances in the terahertz frequency territory. The full-vector finite element method (FV-FEM) is used for the numerical investigation about the competence of the proposed PCF. Simulation results show the maximum relative sensitivity as well as less connement loss for various chemicals. Numerically processed outcomes apparent that the proposed sensor shows 63.24 %, 61.05 % and 60.03 % detecting reactions for focused analytes benzene, ethanol and water individually at 1 THz frequency. Major purposes is to use H-PCF as the application of chemical sensor in commercial sector to detect harmful chemicals.

关键词： Sensitivity Ethanol Sensors Chemicals Terahertz radiation Optical losses Photonic crystals

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WEB-technology for Medical Images Segmentation 3

WEB-technology for Medical Images Segmentation

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3rd Russian-Pacific conference on Computer Technology and Applications (RPC)

作者： Doronicheva, Anna V. Savin, Sergey Z. Russian Acad Sci Far Eastern Branch Ctr Comp Fed State Budgetary Inst Sci Khabarovsk Russia

ISBN: (纸本)9781538675311

In work is described practical using of WEB-technology for segmentation and analysis tasks of medical image. Progress in the development of bioinformatics and mathematical methods in biomedicine, as well as the development of computer and telecommunications systems and networks determines the look of the present and future of medical technology and of medicine in general [8, 10]. At last years of one of the directions of development of cloud, computing technologies in high-tech-medicine is a processing the digital image: improvement of quality of image, recovering image, its recognition of separate elements. recognition of pathological processes is one of the most important problems of processing the medical image. By now, a number of standards for medical imaging have been developed. By analogy with CAD/CAM systems (computer aided design and computer aided manufacturing) for technical applications, CAD (computer-aided diagnosis) systems are being developed for medical purposes. Some of them are already successfully operating, but to date these systems are only "assistants" of a diagnostician who takes decisions. CAD algorithms for medical imaging systems typically include image segmentation, the selection of some objects of interest ("masses"), their analysis, parametric description of the selected objects and their classification.

关键词： expert system computer-aided diagnosis (CAD) pattern recognition medical image scintigraphy

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A Real-Time Detection Drone Algorithm Based on Instance Semantic Segmentation 19

A Real-Time Detection Drone Algorithm Based on Instance Sema...

引用

Proceedings of the 3rd international conference on Video and Image Processing

作者： Zihao Liu Haiqin Xu Yihong Zhang Zhouyi Xu Sen Wu Di Zhu College of Information Science and Technology Engineering Research Center of Digitized Textile & Fashion Technology Ministry of Education DongHua University Shanghai China Huawei Technologies Co. Ltd. Pudong Shanghai China

ISBN: (纸本)9781450376822

With the rapid development of drones, drones are widely used in various fields and bring convenience to people's production and life. However, they also bring security problems to society and the country. Especially in airports or military areas, the flight of drones can cause some problems. In order to effectively supervise the drone, this paper proposes a real-time detection drone algorithm HR-YOLACT which is based on instance semantic segmentation, and designed a new drone data set. The proposed algorithm combines the real-time instance semantic segmentation algorithm YOLACT with the deep high-resolution representation classification network HRNet. Firstly, feature maps are extracted by HRNet's backbone network. Secondly, the feature pyramid network is used to further extract image features, so that the network has better classification ability. Finally, the improved prediction head is utilized to detect the boxes of drones. In addition, this paper uses cross entropy instead of focal loss as the loss function to obtain better network training speed and quality. The experimental results show that HR-YOLACT has faster detection speed and higher detection precision than existing popular real-time object detection and real-time instance semantic segmentation algorithms.

关键词： Drones detection instance semantic segmentation pattern recognition

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Facial Expression recognition Based on G-2DPCA Feature and Sparse Representation Classification 3

Facial Expression Recognition Based on G-2DPCA Feature and S...

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IEEE 3rd international conference on Signal and Image Processing (ICSIP)

作者： Wang, Yan Li, Ming Zhang, Congxuan Chen, Hao Nanchang Hangkong Univ Key Lab Jiangxi Prov Image Proc & Pattern Recogni Nanchang Jiangxi Peoples R China Nanchang Hangkong Univ Engn Training Ctr Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781538663967

In this paper, we proposed an effective method of facial expression recognition based on a G-2DPCA feature and Sparse Representation-based Classification (SRC). Gabor filters with five scales and eight directions are first employed for feature extraction. To address the high dimension of Gabor features, we select one out of forty Gabor filters with an optimal parameter pair of scales and directions to filter facial images. Two-dimensional principal component analysis (2DPCA) is utilized for image representation and data dimension reduction. It retains the 2D geometric structure of an image, and the image matrix does not need to transform into a vector, which reducing the computation time greatly. Finally, Gabor plus 2DPCA (G-2PCA) features are regarded as the atoms of dictionary in SRC. Experimental results demonstrate that the proposed method has better performance than the existing algorithms.

关键词： facial expression recognition Gabor wavelet 2DPCA SRC

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Cyclic Cumulant Based on Communication Signal Multilayer Neural Network Modulation pattern recognition 3

Cyclic Cumulant Based on Communication Signal Multilayer Neu...

引用

3rd IEEE international conference on Cloud Computing and Internet of Things, CCIOT 2018

作者： Qingyang, Guan Yue, Zhang Shenyang Aerospace University College of Electronic and Information Engineering Shenyang China

ISBN: (纸本)9781538671412

After deducing and simulating the cyclic spectrum theory of MPSK and MQAM modulated signals, the cyclic spectrum of the modulated signal is preprocessed and the original feature set is formed as the input data of the CNN. With feature extraction of cyclic spectrum from convolution and subsampling layers of CNN, the modulation signal can be effectively identified within and between classes. The experimental simulation results show that the classification and recognition algorithm of communication signal modulation mode proposed in this paper has better modulation recognition accuracy than other traditional modulation recognition algorithms in the case of low SNR. And when the signal to noise ratio is lower than 5db and higher than-5db, the modulation pattern recognition accuracy can be reached up to 92%. © 2018 IEEE.

关键词： Multilayer neural networks

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Resistive coupled VO₂ oscillators for image recognition

Resistive coupled VO<sub>2</sub> oscillators for image recog...

引用

3rd IEEE international conference on Rebooting Computing (ICRC)

作者： Corti, Elisabetta Gotsmann, Bernd Moselund, Kirsten Stolichnov, Igor Ionescu, Adrian Karg, Siegfried IBM Res Zurich Ruschlikon Switzerland Ecole Polytech Fed Lausanne Nanoelect Devices Lab Lausanne Switzerland

ISBN: (纸本)9781538691700

Oscillator networks are known for their interesting collective behavior such as frequency locking, phase locking, and synchronization. Compared to other artificial neural network implementations, timing rather than amplitude information is used for computation. We have fabricated and simulated small networks of coupled VO2 oscillators and investigated the electrical behavior. It is demonstrated experimentally and through simulations that the coupled oscillators lock in frequency and the phase relation can be adjusted by the coupling resistance. pattern recognition was simulated in resistor-coupled networks with up to nine oscillators (pixels), demonstrating the possibility of implementation of this task with compact VO2 circuits.

关键词： neural network oscillating phase timing resistor-coupled pattern recognition

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Application of data mining for behavior pattern recognition in telecommunication 3rd

Application of data mining for behavior pattern recognition ...

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3rd international conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th international conference on Swarm Intelligence, ICSI 2018

作者： Wu, Xingshen Zhao, Yu Gu, Qiang Gao, Li China Mobile Group Jiangsu Co. Ltd Nanjing China China Mobile Research Institute Beijing China

ISBN: (纸本)9783319938028

In telecom industry, mobile subscribers produce data traffic while online every day. These data traffic suggests that certain characteristics of the behavior. The application of data mining helped to analyze and identify the features from the data traffic. In this paper, we use exponential binning of data preprocessing technology to smooth the data sets and keep reduce the noise. By using K-means algorithm to cluster the data traffic stream, we aim to mining subscribers’ behavior characteristics from clusters, provide support for churn prediction, target marketing, and fraud detection. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Data mining

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